Automatic Personality Perception

Whenever we meet a person for the first time, we tend to attribute personality traits to him/her. The process takes place in less than a second and it is spontaneous and unconscious. While not necessarily accurate (attributed traits do not necessarily correspond to the actual traits of the person), the process still influences significantly our behavior towards others, especially when it comes to social interactions. The phenomenon is so pervasive that it takes place not only when we meet others in person, but also when we observe them in video recordings or we simply interact with an artifact displaying human-like behaviors (robots, artificial agents, animated characters, etc.). This thesis focuses on one aspect of the phenomenon above, namely the spontaneous attribution of personality traits to speakers we never heard before. The prediction of traits attributed to others is important because it can help to better understand the social behavior of people. Furthermore, it can help to design artificial agents capable of eliciting the perception of predefined desirable traits. The experiments of the Thesis are performed over the Speaker Personality Corpus, a collection of 640 speech clips (322 identities in total) annotated in terms of personality traits by 11 assessors. The dataset was used not only for the experiments of this work, but also in an international benchmarking campaign (The Speaker Trait Challenge at INTERSPEECH 2012) allowing the comparison of several approaches proposed by different groups. The thesis includes two main achievements. The first is an approach capable of predicting whether a speaker is perceived to be in the upper or lower part of the observed scores along the Big-Five Traits, the personality dimensions known to capture most of the individual differences. The second is an approach for automatically ordering pairs of speakers along the Big Five Traits, a task that better fits the actual cognitive processes aimed at personality perception. The pairwise ranking scheme relies on an Ordinal Regression approach based on the Ordered Logit Model and on Gaussian Processes. A new, fully Bayesian approach was developed for inferring the parameters of the model.